
from __future__ import annotations

from typing import Any

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							int    ,						snake_case_			:							int   ):
							UpperCamelCase_:				List[str]						=    num_of_nodes
							UpperCamelCase_:				list[list[int]]						=    []
							UpperCamelCase_:				dict[int, int]						=    {}

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int   ):
							self.m_edges.append([u_node, v_node, weight]   )

			def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							int   ):

							if self.m_component[u_node] == u_node:
											return u_node
							return self.find_component(self.m_component[u_node]   )

			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							int   ):

							if self.m_component[u_node] != u_node:
											for k in self.m_component:
															UpperCamelCase_:				Tuple						=    self.find_component(snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							list[int]    ,						snake_case_			:							int    ,						snake_case_			:							int   ):

							if component_size[u_node] <= component_size[v_node]:
											UpperCamelCase_:				Union[str, Any]						=    v_node
											component_size[v_node] += component_size[u_node]
											self.set_component(snake_case_   )

							elif component_size[u_node] >= component_size[v_node]:
											UpperCamelCase_:				List[Any]						=    self.find_component(snake_case_   )
											component_size[u_node] += component_size[v_node]
											self.set_component(snake_case_   )




			def    lowerCAmelCase__						(							self			:							Dict   ):
							UpperCamelCase_:				int						=    []
							UpperCamelCase_:				Any						=    0

							UpperCamelCase_:				list[Any]						=    [-1] * self.m_num_of_nodes

							# A list of components (initialized to all of the nodes)
							for node in range(self.m_num_of_nodes   ):
											self.m_component.update({node: node}   )
											component_size.append(1   )

							UpperCamelCase_:				Optional[int]						=    self.m_num_of_nodes

							while num_of_components > 1:
											for edge in self.m_edges:
															UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    edge

															UpperCamelCase_:				int						=    self.m_component[u]
															UpperCamelCase_:				Dict						=    self.m_component[v]

															if u_component != v_component:

																			for component in (u_component, v_component):
																							if (
																							    minimum_weight_edge[component] == -1
																							    or minimum_weight_edge[component][2] > w
																							):
																											UpperCamelCase_:				str						=    [u, v, w]

											for edge in minimum_weight_edge:
															if isinstance(snake_case_    ,						snake_case_   ):
																			UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Dict						=    edge

																			UpperCamelCase_:				Any						=    self.m_component[u]
																			UpperCamelCase_:				Union[str, Any]						=    self.m_component[v]

																			if u_component != v_component:
																							mst_weight += w
																							self.union(snake_case_    ,						snake_case_    ,						snake_case_   )
																							print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''   )
																							num_of_components -= 1

											UpperCamelCase_:				str						=    [-1] * self.m_num_of_nodes
							print(f'''The total weight of the minimal spanning tree is: {mst_weight}'''   )







def 							A__       (    )	->						None:
				pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :			Union[str, Any]    =	{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			List[Any]    =	[
							    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TapasForMaskedLM""",
							    """TapasForQuestionAnswering""",
							    """TapasForSequenceClassification""",
							    """TapasModel""",
							    """TapasPreTrainedModel""",
							    """load_tf_weights_in_tapas""",
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[Any]    =	[
							    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFTapasForMaskedLM""",
							    """TFTapasForQuestionAnswering""",
							    """TFTapasForSequenceClassification""",
							    """TFTapasModel""",
							    """TFTapasPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Any    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






